An LLM-powered multi-agent system: Enabling real-time cognitive and psychological state assessment in K-12 mathematics learning

Resumo

As artificial intelligence (AI) continues to transform education, Multi-Agent Systems (MAS) are emerging as a promising framework for delivering adaptive, personalized, and scalable learning experiences. While traditional MAS have demonstrated capabilities in instructional delivery and basic emotion detection, they remain limited in their ability to dynamically and accurately assess students’ real-time cognitive load and psychological states. Addressing these gaps, this study proposes a next-generation MAS architecture that integrates a Learning-by-Teaching large language model (LLM) agent, a Real-Time Cognitive Load Detection agent based on natural language processing (NLP), and a Questionnaire-Integrated Holistic Analysis agent. To develop and validate this system, we conducted an empirical study within Alter-Math, an AI-augmented mathematics learning platform designed to support dialogic, learning-by-teaching interactions in K-12 mathematics education, collaborating with local middle schools to recruit 155 students. During structured learning sessions, students engaged in teaching mathematical concepts to the LLM agent, generating rich linguistic interaction data, complemented by pre- and post-intervention measures of cognitive load, stress, confidence, and related psychological factors. These multimodal datasets informed the training of NLP-based classifiers for unobtrusive, real-time monitoring of learner states. Results suggest the potential of the proposed MAS framework to enhance the responsiveness and psychological awareness of AI-driven educational systems, paving the way for more personalized and cognitively supportive learning environments.

Dados da obra

Tipo

Artigo

Periódico

Educational Technology & Society, v. 29, n. 2, p. 404-421

Editora

International Forum of Educational Technology & Society

Ano

2026 (2026-04)

Idioma

en

Palavras-chave

llms; mas; real-time cognitive assessment; natural language processing; k-12 mathematics education

Temas

Avaliação

DOI

10.30191/ets.202604_29(2).sp10

ISSN

1176-3647, 1436-4522

Acesso

Acesso aberto

Fontes

DOAJ — Directory of Open Access Journals

Na Biblioteca desde

01/10/2026 · verificada em 03/10/2026